import h5py

import math

import nibabel as nib

import numpy as np

from medpy import metric

import torch

import torch.nn.functional as F

from tqdm import tqdm

from skimage.measure import label

def getLargestCC(segmentation):

    labels = label(segmentation)

    assert( labels.max() != 0 ) # assume at least 1 CC

    largestCC = labels == np.argmax(np.bincount(labels.flat)[1:])+1

    return largestCC

def var_all_case(model, num_classes, patch_size=(112, 112, 80), stride_xy=18, stride_z=4, dataset_name="LA"):

    if dataset_name == "LA":

        p = '/data/omnisky/postgraduate/Yb/data_set/LASet'

        with open(p+'/test.list', 'r') as f:

            image_list = f.readlines()

        image_list = [p+"/2018LA_Seg_Training Set/" + item.replace('\n', '') + "/mri_norm2.h5" for item in image_list]

    elif dataset_name == "Pancreas_CT":

        with open('./data/Pancreas/test.list', 'r') as f:

            image_list = f.readlines()

        image_list = ["./data/Pancreas/Pancreas_h5/" + item.replace('\n', '') + "_norm.h5" for item in image_list]

    loader = tqdm(image_list)

    total_dice = 0.0

    for image_path in loader:

        h5f = h5py.File(image_path, 'r')

        image = h5f['image'][:]

        label = h5f['label'][:]

        prediction, score_map = test_single_case_first_output(model, image, stride_xy, stride_z, patch_size, num_classes=num_classes)

        if np.sum(prediction)==0:

            dice = 0

        else:

            dice = metric.binary.dc(prediction, label)

        total_dice += dice

    avg_dice = total_dice / len(image_list)

    print('average metric is {}'.format(avg_dice))

    return avg_dice

def test_all_case(model_name, num_outputs, model, image_list, num_classes, patch_size=(112, 112, 80), stride_xy=18, stride_z=4, save_result=True, test_save_path=None, preproc_fn=None, metric_detail=1, nms=0):

    loader = tqdm(image_list) if not metric_detail else image_list

    ith = 0

    total_metric = 0.0

    total_metric_average = 0.0

    for image_path in loader:

        h5f = h5py.File(image_path, 'r')

        image = h5f['image'][:]

        label = h5f['label'][:]

        if preproc_fn is not None:

            image = preproc_fn(image)

        prediction, score_map = test_single_case_first_output(model, image, stride_xy, stride_z, patch_size, num_classes=num_classes)

        if num_outputs > 1:

            prediction_average, score_map_average = test_single_case_average_output(model, image, stride_xy, stride_z, patch_size, num_classes=num_classes)

        if nms:

            prediction = getLargestCC(prediction)

            if num_outputs > 1:

                 prediction_average = getLargestCC(prediction_average)

        if np.sum(prediction)==0:

            single_metric = (0,0,0,0)

            if num_outputs > 1:

                single_metric_average = (0,0,0,0)

        else:

            single_metric = calculate_metric_percase(prediction, label[:])

            if num_outputs > 1:

                single_metric_average  = calculate_metric_percase(prediction_average, label[:])

        if metric_detail:

            print('%02d,\t%.5f, %.5f, %.5f, %.5f' % (ith, single_metric[0], single_metric[1], single_metric[2], single_metric[3]))

            if num_outputs > 1:

                print('%02d,\t%.5f, %.5f, %.5f, %.5f' % (ith, single_metric_average[0], single_metric_average[1], single_metric_average[2], single_metric_average[3]))

        total_metric += np.asarray(single_metric)

        if num_outputs > 1:

            total_metric_average += np.asarray(single_metric_average) 

        if save_result:

            nib.save(nib.Nifti1Image(prediction.astype(np.float32), np.eye(4)), test_save_path +  "%02d_pred.nii.gz" % ith)

            nib.save(nib.Nifti1Image(score_map[0].astype(np.float32), np.eye(4)), test_save_path +  "%02d_scores.nii.gz" % ith)

            if num_outputs > 1:

                nib.save(nib.Nifti1Image(prediction_average.astype(np.float32), np.eye(4)), test_save_path +  "%02d_pred_average.nii.gz" % ith)

                nib.save(nib.Nifti1Image(score_map_average[0].astype(np.float32), np.eye(4)), test_save_path +  "%02d_scores_average.nii.gz" % ith)

            nib.save(nib.Nifti1Image(image[:].astype(np.float32), np.eye(4)), test_save_path +  "%02d_img.nii.gz" % ith)

            nib.save(nib.Nifti1Image(label[:].astype(np.float32), np.eye(4)), test_save_path +  "%02d_gt.nii.gz" % ith)

        ith += 1

    avg_metric = total_metric / len(image_list)

    print('average metric is decoder 1 {}'.format(avg_metric))

    if num_outputs > 1:

        avg_metric_average = total_metric_average / len(image_list)

        print('average metric of all decoders is {}'.format(avg_metric_average))

    with open(test_save_path+'../{}_performance.txt'.format(model_name), 'w') as f:

        f.writelines('average metric of decoder 1 is {} \n'.format(avg_metric))

        if num_outputs > 1:

            f.writelines('average metric of all decoders is {} \n'.format(avg_metric_average))

    return avg_metric

def test_single_case_first_output(model, image, stride_xy, stride_z, patch_size, num_classes=1):

    w, h, d = image.shape

    # if the size of image is less than patch_size, then padding it

    add_pad = False

    if w < patch_size[0]:

        w_pad = patch_size[0]-w

        add_pad = True

    else:

        w_pad = 0

    if h < patch_size[1]:

        h_pad = patch_size[1]-h

        add_pad = True

    else:

        h_pad = 0

    if d < patch_size[2]:

        d_pad = patch_size[2]-d

        add_pad = True

    else:

        d_pad = 0

    wl_pad, wr_pad = w_pad//2,w_pad-w_pad//2

    hl_pad, hr_pad = h_pad//2,h_pad-h_pad//2

    dl_pad, dr_pad = d_pad//2,d_pad-d_pad//2

    if add_pad:

        image = np.pad(image, [(wl_pad,wr_pad),(hl_pad,hr_pad), (dl_pad, dr_pad)], mode='constant', constant_values=0)

    ww,hh,dd = image.shape

    sx = math.ceil((ww - patch_size[0]) / stride_xy) + 1

    sy = math.ceil((hh - patch_size[1]) / stride_xy) + 1

    sz = math.ceil((dd - patch_size[2]) / stride_z) + 1

    # print("{}, {}, {}".format(sx, sy, sz))

    score_map = np.zeros((num_classes, ) + image.shape).astype(np.float32)

    cnt = np.zeros(image.shape).astype(np.float32)

    for x in range(0, sx):

        xs = min(stride_xy*x, ww-patch_size[0])

        for y in range(0, sy):

            ys = min(stride_xy * y,hh-patch_size[1])

            for z in range(0, sz):

                zs = min(stride_z * z, dd-patch_size[2])

                test_patch = image[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]]

                test_patch = np.expand_dims(np.expand_dims(test_patch,axis=0),axis=0).astype(np.float32)

                test_patch = torch.from_numpy(test_patch).cuda()

                with torch.no_grad():

                    y = model(test_patch)

                    if len(y) > 1:

                        y = y[0]

                    y = F.softmax(y, dim=1)

                y = y.cpu().data.numpy()

                y = y[0,1,:,:,:]

                score_map[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \

                  = score_map[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + y

                cnt[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \

                  = cnt[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + 1

    score_map = score_map/np.expand_dims(cnt,axis=0)

    label_map = (score_map[0]>0.5).astype(np.int)

    if add_pad:

        label_map = label_map[wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]

        score_map = score_map[:,wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]

    return label_map, score_map

def test_single_case_average_output(net, image, stride_xy, stride_z, patch_size, num_classes=1):

    w, h, d = image.shape

    # if the size of image is less than patch_size, then padding it

    add_pad = False

    if w < patch_size[0]:

        w_pad = patch_size[0]-w

        add_pad = True

    else:

        w_pad = 0

    if h < patch_size[1]:

        h_pad = patch_size[1]-h

        add_pad = True

    else:

        h_pad = 0

    if d < patch_size[2]:

        d_pad = patch_size[2]-d

        add_pad = True

    else:

        d_pad = 0

    wl_pad, wr_pad = w_pad//2,w_pad-w_pad//2

    hl_pad, hr_pad = h_pad//2,h_pad-h_pad//2

    dl_pad, dr_pad = d_pad//2,d_pad-d_pad//2

    if add_pad:

        image = np.pad(image, [(wl_pad,wr_pad),(hl_pad,hr_pad), (dl_pad, dr_pad)], mode='constant', constant_values=0)

    ww,hh,dd = image.shape

    sx = math.ceil((ww - patch_size[0]) / stride_xy) + 1

    sy = math.ceil((hh - patch_size[1]) / stride_xy) + 1

    sz = math.ceil((dd - patch_size[2]) / stride_z) + 1

    # print("{}, {}, {}".format(sx, sy, sz))

    score_map = np.zeros((num_classes, ) + image.shape).astype(np.float32)

    cnt = np.zeros(image.shape).astype(np.float32)

    for x in range(0, sx):

        xs = min(stride_xy*x, ww-patch_size[0])

        for y in range(0, sy):

            ys = min(stride_xy * y,hh-patch_size[1])

            for z in range(0, sz):

                zs = min(stride_z * z, dd-patch_size[2])

                test_patch = image[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]]

                test_patch = np.expand_dims(np.expand_dims(test_patch,axis=0),axis=0).astype(np.float32)

                test_patch = torch.from_numpy(test_patch).cuda()

                with torch.no_grad():

                    y_logit = net(test_patch)

                    num_outputs = len(y_logit)

                    y=torch.zeros(y_logit[0].shape).cuda()

                    for idx in range(num_outputs):

                        y += y_logit[idx]

                    y/=num_outputs

                y = y.cpu().data.numpy()

                y = y[0,1,:,:,:]

                score_map[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \

                  = score_map[:, xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + y

                cnt[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] \

                  = cnt[xs:xs+patch_size[0], ys:ys+patch_size[1], zs:zs+patch_size[2]] + 1

    score_map = score_map/np.expand_dims(cnt,axis=0)

    label_map = (score_map[0]>0.5).astype(np.int)

    if add_pad:

        label_map = label_map[wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]

        score_map = score_map[:,wl_pad:wl_pad+w,hl_pad:hl_pad+h,dl_pad:dl_pad+d]

    return label_map, score_map

def calculate_metric_percase(pred, gt):

    dice = metric.binary.dc(pred, gt)

    jc = metric.binary.jc(pred, gt)

    hd = metric.binary.hd95(pred, gt)

    asd = metric.binary.asd(pred, gt)

    return dice, jc, hd, asd